Healthcare
LLM applications that transcribe clinical conversations, generate structured notes, draft discharge summaries, and reduce the documentation burden that consumes clinician time.
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Industry overview
Generative AI tools for clinical documentation — transcribing consultations to structured notes, generating discharge summaries and referral letters, and assisting with clinical coding — to reduce the administrative burden on clinicians.
At a glance
Clinicians spend between 30–50% of their working time on documentation — writing notes, completing discharge summaries, drafting referral letters, and assigning clinical codes. This is time that does not benefit patients directly. Ambient AI documentation tools reduce this burden by generating structured notes from clinical conversations — allowing clinicians to focus on the clinical interaction rather than the administrative record of it.
We develop ambient clinical note generation systems that transcribe and structure consultation recordings into specialty-specific documentation formats. Discharge summary drafting tools generate structured summaries from EHR data that clinicians review and approve rather than write from scratch. Referral letter generation creates outgoing referrals from consultation findings. ICD and SNOMED coding assistance tools suggest codes from clinical narrative to support accurate and complete coding. All applications integrate with EHR systems via HL7 FHIR APIs and include clinician review and approval workflows — AI drafts; clinicians approve.
Key capabilities
Engagements are scoped to your business context — these are the core capabilities we bring to healthcare clients.
Ambient clinical note generation from consultation recordings
Discharge summary drafting from EHR data for clinician review
Referral letter generation from consultation findings
ICD and SNOMED coding assistance from clinical narrative
Specialty-specific documentation templates (GP, orthopaedics, cardiology)
EHR integration via HL7 FHIR API with clinician approval workflow
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